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Open Data from the Third Observing Run of LIGO, Virgo, KAGRA, and GEO

Astrophysical Journal, Supplement Series 2023年第2期267卷 29-29页

作者： Abbott, R. Abe, H. Acernese, F. Ackley, K. Adhicary, S. Adhikari, N. Adhikari, R.X. Adkins, V.K. Adya, V.B. Affeldt, C. Agarwal, D. Agathos, M. Aguiar, O.D. Aiello, L. Ain, A. Ajith, P. Akutsu, T. Albanesi, S. Alfaidi, R.A. Al-Jodah, A. Alléné, C. Allocca, A. Almualla, M. Altin, P.A. Amato, A. Amez-Droz, L. Amorosi, A. Anand, S. Ananyeva, A. Andersen, R. Anderson, S.B. Anderson, W.G. Andia, M. Ando, M. Andrade, T. Andres, N. Andrés-Carcasona, M. Andrić, T. Ansoldi, S. Antelis, J.M. Antier, S. Aoumi, M. Apostolatos, T. Appavuravther, E.Z. Appert, S. Apple, S.K. Arai, K. Araya, A. Araya, M.C. Areeda, J.S. Arène, M. Aritomi, N. Arnaud, N. Arogeti, M. Aronson, S.M. Arun, K.G. Asada, H. Ashton, G. Aso, Y. Assiduo, M. Assis de Souza Melo, S. Aston, S.M. Astone, P. Aubin, F. AultONeal, K. Babak, S. Badalyan, A. Badaracco, F. Badger, C. Bae, S. Bagnasco, S. Bai, Y. Baier, J.G. Baiotti, L. Baird, J. Bajpai, R. Baka, T. Ball, M. Ballardin, G. Ballmer, S.W. Baltus, G. Banagiri, S. Banerjee, B. Bankar, D. Baral, P. Barayoga, J.C. Barber, J. Barish, B.C. Barker, D. Barneo, P. Barone, F. Barr, B. Barsotti, L. Barsuglia, M. Barta, D. Barthelmy, S.D. Barton, M.A. Bartos, I. Basak, S. Basalaev, A. Bassiri, R. Basti, A. Bawaj, M. Bayley, J.C. Baylor, A.C. Bazzan, M. Bécsy, B. Bedakihale, V.M. Beirnaert, F. Bejger, M. Bell, A.S. Benedetto, V. Beniwal, D. Benoit, W. Bentley, J.D. Ben Yaala, M. Bera, S. Berbel, M. Bergamin, F. Berger, B.K. Bernuzzi, S. Beroiz, M. Berry, C.P.L. Bersanetti, D. Bertolini, A. Betzwieser, J. Beveridge, D. Bevins, N. Bhandare, R. Bhandari, A.V. Bhardwaj, U. Bhatt, R. Bhattacharjee, D. Bhaumik, S. Bianchi, A. Bilenko, I.A. Bilicki, M. Billingsley, G. Bini, S. Birnholtz, O. Biscans, S. Bischi, M. Biscoveanu, S. Bisht, A. Biswas, B. Bitossi, M. Bizouard, M.-A. Blackburn, J.K. Blair, C.D. Blair, D.G. Blair, R.M. Bobba, F. Bode, N. Boër, M. Bogaert, G. Boileau, G. Boldrini, M. Bolingbroke, G.N. Bonavena, L.D. Bondarescu, R. Bondu, F. Bonilla, E. Bonilla, G.S. Bonnand, R. Booker, P. Bork, R. Boschi, V. Bose, N. LIGO Laboratory California Institute of Technology Pasadena 91125 CA United States Graduate School of Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo 152-8551 Japan Dipartimento di Farmacia Università di Salerno Salerno Fisciano I-84084 Italy INFN Sezione di Napoli Napoli I-80126 Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 VIC Australia The Pennsylvania State University University Park 16802 PA United States University of Wisconsin-Milwaukee Milwaukee 53201 WI United States Louisiana State University Baton Rouge 70803 LA United States OzGrav Australian National University Canberra 0200 ACT Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Hannover D-30167 Germany Leibniz Universität Hannover Hannover D-30167 Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Instituto Nacional de Pesquisas Espaciais São Paulo São José dos Campos 12227-010 Brazil Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa Pisa I-56127 Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Dipartimento di Fisica Università degli Studi di Torino Torino I-10125 Italy INFN Sezione di Torino Torino I-10125 Italy SUPA University of Glasgow Glasgow G12 8QQ United Kingdom OzGrav University of Western Australia Crawley 6009 WA Australia Univ. Savoie Mont Blanc CNRS Laboratoire d'Annecy de Physique des Particules - IN2P3 Annecy F-74000 France Università di Napoli “Federico II” Napoli I-80126 Italy University of Minnesota Minneapolis 55455 MN United States M

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://***. The main data set, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages. © 2023. The Author(s). Published by the American Astronomical Society.

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A Survey on Big Data for Human Body Shape

A Survey on Big Data for Human Body Shape

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Hongli Peng Xiuqin Shang Chao Guo Gang Xiong Timo R.Nyberg Dong Fan Yiming Wang The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences College of Information Science and Technology Beijing University of Chemical Technology Qingdao Academy of Intelligent Industries Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of Sciences Cloud Computing Center Chinese Academy of Sciences Department of Industrial Engineering and Management School of ScienceAalto University

In recent years, the rapid development of network and E-commerce technologies, promotes the development of human body big data. New technologies, such as 3D scanning and 3D printing will improve our life quality. The new socialized manufacturing network composed of Io T, advanced logistics network and 3D printers. Everyone is allowed to have chance to participate in any process of the whole production manufacturing lifecycle by some ways, such as crowdsourcing. Boosts the creation, production and consumption patterns of individuation, real-time transformation and E-commerce, possibly will lead to a new industrial revolution. In this revolutionary change wave, public concerns focus on the human body big data used for social manufacturing of wearable products or personalized medical treatment. Although many scholars have done a lot of research work, the collection of the human body shape big data still faces many challenges. There is no complete, separated theoretical research works in progress, it should be summarized and improved systematically. In this paper, we review that big data for human body shape is gaining momentum. The 3D scanning and 3D modeling of human body shape are summarized and concluded. The main applications of these big data are analyzed.

关键词： big data human body shape 3D scanning 3D modeling clothing footwear mass customization

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The Application of the Depth Camera in the Social Manufacturing: A review

The Application of the Depth Camera in the Social Manufactur...

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Zhen Liang Ni Zhen Shen Chao Guo Gang Xiong Timo R.Nyberg Xiuqin Shang Shuangshuang Li Yiming Wang The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences College of Information Science and Technology Beijing University of Chemical Technology Cloud Computing Center Chinese Academy of Sciences Qingdao Academy of Intelligent Industries Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of Sciences Department of Industrial Engineering and Management School of ScienceAalto University

In recent years, the information Technology develops rapidly. With the development of the information Technology, the social manufacturing, a new mode of manufacturing, has been proposed. The social manufacturing combines the Internet technology, the three-dimensional scanning technology and the virtual reality technology, and is used in the apparel industry and other fashion industries for the mass customization. The three-dimensional scanning technology and the virtual fitting mirror technology are inseparable from the depth camera. Depth cameras play an important role in the social manufacturing. Manufacturers use depth cameras to obtain three-dimensional models of the body of customers to design suitable clothes. Customers can visually see the effect of clothes by using a virtual fitting mirror which is based on the depth camera. The social manufacturing makes mass customization possible, and the depth camera help develop the social manufacturing. This paper introduces the principles of depth cameras, and the application of depth cameras in the social manufacturing.

关键词： social manufacturing depth camera PMD Kinect time of flight structured light

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Optimized design of the universal ternary gates for quantum/reversible computing

Optimized design of the universal ternary gates for quantum/...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications

作者： Vitaly Deibuk Iryna Turchenko Vladyslav Shults Department of Computer Systems and Networks Chernivtsi National University Chernivtsi Ukraine Department of Information Computing Systems and Control Ternopil National Economic University Ternopil Ukraine

Multiple-valued logic is a promising choice for future computer technologies, which provides a set of advantages comparing to binary circuits. In this paper, we have developed a genetic algorithm-based synthesis of ternary reversible circuits using Muthukrishnan-Stroud gates. The method for chromosomes coding, as well as a reasonable choice of algorithm parameters, allowed obtaining circuits for ternary Toffoli and modified Fredkin gates, which are better than other published methods in terms of quantum cost, delay times and amount of input ancillary and output garbage qutrits.

关键词： Logic gates Genetic algorithms Quantum computing Delays Biological cells Computers Multivalued logic

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A survey on big data for human body shape

A survey on big data for human body shape

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Hongli Peng Xiuqin Shang Chao Guo Gang Xiong Timo R. Nyberg Dong Fan Yiming Wang The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Qingdao Academy of Intelligent Industries Qingdao China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China Cloud Computing Center Chinese Academy of Sciences China Guangdong Department of Industrial Engineering and Management Aalto University Finland SBoleni Intelligent Solutions Technology (SuZhou) Co. Ltd Joint Laboratory of Intelligent IoT & Cloud for Parallel Management & Control Suzhou China

ISBN: (纸本)9781509029280

In recent years, the rapid development of network and E-commerce technologies, promotes the development of human body big data. New technologies, such as 3D scanning and 3D printing will improve our life quality. The new socialized manufacturing network composed of IoT, advanced logistics network and 3D printers. Everyone is allowed to have chance to participate in any process of the whole production manufacturing lifecycle by some ways, such as crowdsourcing. Boosts the creation, production and consumption patterns of individuation, real-time transformation and E-commerce, possibly will lead to a new industrial revolution. In this revolutionary change wave, public concerns focus on the human body big data used for social manufacturing of wearable products or personalized medical treatment. Although many scholars have done a lot of research work, the collection of the human body shape big data still faces many challenges. There is no complete, separated theoretical research works in progress, it should be summarized and improved systematically. In this paper, we review that big data for human body shape is gaining momentum. The 3D scanning and 3D modeling of human body shape are summarized and concluded. The main applications of these big data are analyzed.

关键词： Three-dimensional displays Feature extraction Shape Biological system modeling Solid modeling Computational modeling Data models

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Computational Molecular Design of Sulfonamides based on Immune Network Modeling

Computational Molecular Design of Sulfonamides based on Immu...

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International Conference on Disaster Recovery and Business Continuity

作者： Galina Samigulina Zarina Samigulina Chef of the laboratory 《Intellectual control systems and forecasting》 Institute of Information and Computing Technologies IICT Department 《Automation and control》 Kazakh National Research Technical University KRNTU

ISBN: (纸本)9781509002016

Nowadays, the establishment of effective methods of QSAR (Quantitative Structure Activity Relationships) to predict the properties of new chemical compounds and directional computational molecular design of drug compounds are the most important and urgent tasks of bioinformatics. The article proposes a technology based on immune network modeling to determine the substance of new drug compounds. There was presented the algorithm for the modeling of the dependences "structure-property" on the example of sulfonamides with different duration of action based on the biological approach of artificial immune systems. There was designed component-oriented approach for computational molecular design of new drug compounds based on immune network modeling. The proposed intelligent technology allows to analyze the hidden (latent) relations between the descriptors, to decrease the training time for the immune network and to use the descriptors of different levels.

关键词： Computational molecular design Immune network modeling Sulfonamides Component-oriented software

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Reliability improvement and reliability assessment for distributed hardware-software multi-agent systems

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WSEAS Transactions on Computers 2014年 13卷 670-690页

作者： Igumnov, Alexei V. Saradgishvili, Sergey E. Information and Control Systems Department Institute of Computing and Control Saint-Petersburg State Polytechnical University 29 Polytechnicheskaya Street Saint-Petersburg Russia

The problem of reliability of new object of distributed hardware software (DHS) multi-agent system (MAS) is considered. DHS MAS is determined as a system that is based on agent technologies and consists of both agents and hardware components required for execution of agents and for interaction of agents with an environment. Reliability improvement, fault-recovery and several reliability assessment approaches for DHS MAS are presented. The reliability improvement methodology is built upon replication of unique functional components and redundancy of universal components. The fault-recovery methodology defines a set of fault-recovery procedures required for restoration of consistent system configuration after failures of its components. Methodology for operability function formation was developed to enable utilization of logical-and- probabilistic methods for reliability assessment. Another approach for reliability assessment is based on Markovian model and system state graph and was developed to overcome limitations of logical-andprobabilistic methods that are suitable only for systems with hot standby. The state graph based approach allows reliability assessment for DHS MAS with cold standby and different operating modes of system components. The state graph based approach is also applicable for a case when probability of failure of one of components depends on states of other components. New failure model for determination of failure rates of system components in accordance with system state is introduced. computing experiments described in the article have validated developed methodologies.

关键词： Multi agent systems

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Real time object tracking via a mixture model

Real time object tracking via a mixture model

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Dongxu Gao Zhaojie Ju Jiangtao Cao Honghai Liu Intelligent Systems and Biomedical Robotics group School of Computing’ University of Portsmouth Portsmouth England Department of Information and Control Engineering Liaoning Shihua University Fushun Liaoning China

Object tracking has been applied in many fields such as intelligent surveillance and computer vision. Although much progress has been made, there are still many puzzles which pose a huge challenge to object tracking. Currently, the problems are mainly caused by appearance model as well as real-time performance. A novel method was been proposed in this paper to handle both of these problems. Locally dense contexts feature and image information (i.e. the relationship between the object and its surrounding regions) are combined in a Bayes framework. Then the tracking problem can be seen as a prediction question which need to compute the posterior probability. Both scale variations and temple updating are considered in the proposed algorithm to assure the effectiveness. To make the algorithm runs in a real time system, a Fourier Transform (FT) is used when solving the Bayes equation. Therefore, the MMOT (Mixture model for object tracking) runs in real-time and performs better than state-of-the-art algorithms on some challenging image sequences in terms of accuracy, quickness and robustness.

关键词： Computational modeling Image color analysis Hidden Markov models Computer vision Object tracking Pattern recognition Real-time systems

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